U.S. Productivity is Rising, but AI Doesn't Seem the Reason

U.S. productivity has been rising for several years, but artificial intelligence is probably not the reason, at least, not yet. 

According to a report published by the Federal Reserve Bank of San Francisco, the U.S. economy expanded at a relatively steady pace of around 2.5 percent per year over the past three years, even though employment growth slowed to near zero. 

Almost by definition, higher output with the same input means higher productivity. But it is not clear artificial intelligence has much to do with the increases.

A survey of nearly 6,000 senior business executives in the United States, United Kingdom, Germany and Australia published by the National Bureau of Economic Research found:

• 69 percent of firms actively use AI

• 66 percent of executives regularly use AI

• Average use is about 1.5 hours a week

• 90 percent of executives report little own-firm impact of AI over the last three years

• 90 percent report no impact on employment or productivity

• Over the next three years, respondents predict that AI will boost productivity at their firms by an average of 1.4 percent

• Will raise output 0.8 percent

• Cut employment 0.7 percent

• Employees believe AI will raise employment 0.5 percent in the next three years.

Perhaps the most-unexpected result is the employee belief that AI will actually boost employment at their firms over a three-year period. That findings seems at odds with the usual press reports suggesting employee angst about AI impact on employment. 

The least-surprising result should be the inability to pinpoint AI productivity gains. 

For starters, U.S. productivity has recently been rising since about 2019, well before AI emerged as a potential driver. 

Labor productivity measures how efficiently workers use the capital available to them, such as equipment or software. The data suggests workers are doing so. 

Total factor productivity uses a broader view, measuring how efficiently the economy uses all inputs together, including both labor and capital.

One interpretation might be that workers have been using tools effectively, but that the gains have not yet shown up in TFP metrics. 

Think about your own work. Many of us would absolutely agree that AI has boosted our own personal productivity. But few of us can point to measurable gains in economic `outputs. 

Federal Reserve Bank 

And U.S. productivity had been rising since about 1992 as well, to 2000. 

Federal Reserve Bank 

For some observers, past experience suggests a productivity gain will happen. The U.S. economy has experienced several distinct productivity regimes over the past 70 years, including a high-growth period in the late 1990s, with the proliferation of computers and the internet, and a lengthy period of low average growth during the 2010s.

Federal Reserve Bank

Right now, it appears there is a significant disconnect between labor productivity and TFP. 

“The divergence between strong labor productivity growth and more modest TFP growth suggests that recent investments related to AI might be making workers more productive by providing them with better tools, such as new software and expanded computing capacity, but broader efficiency gains remain unrealized so far,” the report says.

But the report also says the pattern (Labor productivity and TFP misaligned) resembles what we saw when the internet became important. 

There was a lag then, and there is arguably a lag now. As the adage goes, one can see the impact everywhere but in the outcomes (paraphrasing the Solow Paradox: "You can see the computer age everywhere but in the productivity statistics.").      

Orbital AI Compute Seems to be Coming, but Not at Scale, Right Away

With SpaceX going public on June 12, 2026, lots of investors will be pondering the feasibility of creating orbital data centers at scale.

But space-based data centers are not an immediate replacement for terrestrial data center alternatives for reasons of initial cost and capacity. Launch costs remain substantial.  

Potential upsides center on lower ongoing costs offsetting high upfront costs, eventually, though initial total operating costs will probably not match terrestrial alternatives:

• Cheap/abundant power: Solar in orbit provides ~36% higher irradiance, near-constant supply (no night/clouds/weather), and very low marginal costs (projections ~$0.005/kWh vs. $0.04-0.08/kWh terrestrial wholesale). No grid connection, fuel, or large storage needed in ideal orbits.

• Lower OpEx: Projections include 97% lower operating costs in some models (energy + cooling). No land, permitting, property taxes, or water for cooling. Avoids terrestrial delays/queues for power infrastructure.

• Scalability and utilization: Unlimited "land" in orbit for expansion. High utilization from constant power. Falling launch costs could lead to cost parity or better for power-dominant workloads by late 2020s to 2030s.

Orbital systems could ease some important terrestrial obstacles:

• Energy and emissions: Relies on clean solar (potentially 10x lower CO₂ emissions). Reduces strain on terrestrial grids, which often use fossil backups for data centers.

• Resource Savings: No water consumption for evaporative cooling (a major terrestrial issue). Frees land for other uses; avoids local ecosystem/power price impacts from hyperscale farms.

• Overall footprint: Could lower terrestrial data center growth, helping with power queues, water scarcity, and NIMBY opposition.

Of course, environmental impact is still there. Launch emissions, space debris (cluttering orbits, potential Kessler syndrome risk), manufacturing impacts and end-of-life disposal remain issues. 

Some use cases might make more sense. Workloads tolerant of moderate latency (~100-500 ms round-trip) and benefiting from proximity to space data or constant power suggest suitability for:

• AI Inference: Querying trained models (chat, search, voice agents, video generation, back-office automation)

• Some telemetry use cases: Onboard near-source analysis of Earth observation, climate monitoring, disaster detection (wildfires/floods), maritime surveillance, sensor apps

• Some edge compute cases: Real-time processing for satellites, space cybersecurity or autonomous operations or resilience against terrestrial outages/disasters

• High-Security/ Sovereign Compute: Isolated environments for sensitive data, national security, or regions with poor terrestrial infrastructure.

Public Cloud, Private Cloud or On-Prem for AI Processing?

Among the many other changes artificial computing is raising for enterprise technologists and managers, AI also creates a new framework for thinking about older issues such as "cloud or on-prem?"

The new question is: "Which workloads justify dedicated GPU ownership, and which should be rented?"

Historically, the decision matrix was fairly simple.

Workload	Preferred Location
Stable workloads	Owned infrastructure
Variable workloads	Public cloud

But AI inference operations introduce new variables.

Variable	Why It Matters
GPU utilization rate	Idle GPUs are extremely expensive assets
Data gravity	Moving large datasets can be costly
Security/compliance	Some training data cannot leave enterprise control
Latency requirements	Inference may need proximity to users
Model size	Large models require specialized clusters
Elasticity	Some workloads are highly bursty
Technology obsolescence	GPUs depreciate faster than traditional servers
Capital availability	AI clusters require large up-front investments

So the new decision-making matrix requires some understanding of when public cloud, private cloud or owned facilities provide the best economics for specific workloads.

For example, public cloud remains an optimal choice when utilization is uncertain or sporadic.

AI Task	Advantage
AI experimentation	No capital investment
Proof-of-concept projects	Fast startup
Occasional model training	Rent GPUs only when needed
Seasonal demand spikes	Elastic scaling
Startup AI products	Preserve capital
New model evaluation	Access latest GPUs immediately

If GPU utilization is below roughly 30 percent to 40 percent, public cloud often is economically attractive.

But private cloud (enterprise-owned Infrastructure operated as a cloud) makes sense in other scenarios, such as trials or customer service operations, for reasons including customization, data control or security. 

AI Task

Internal enterprise copilots

Customer service AI

Financial AI applications

Healthcare AI systems

Proprietary model fine-tuning

Enterprise knowledge management AI

If workloads are predictable and GPU utilization exceeds roughly 50 percent to 60 percent, private infrastructure often becomes economically superior.

Owned facilities will make most sense for hyperscalers such as Amazon Web Services, Microsoft Azure, Google Cloud, large AI labs, major telecom operators, or very-large enterprises.

AI Task

Frontier model development

Large-scale foundation model training

Continuous AI training operations

National AI infrastructure

Massive enterprise AI platforms

As often happens with computing technology, no single solution is right for every use case. For most large enterprises, the most-likely long-term architecture for large enterprises will often use public cloud, sometimes private cloud or owned facilities in some instances. 

No solution will always be the best. 

Workload Type	Best Location
AI experiments	Public cloud
Model training bursts	Public cloud
Fine tuning proprietary models	Private cloud
Internal enterprise inference	Private cloud
Regulated data workloads	Private cloud
Consumer-facing inference spikes	Public cloud
Constant high-volume inference	Owned GPU clusters
Mission-critical AI	Hybrid

Pilots and training will normally be best suited for public cloud platforms. Proprietary models, regulated workloads or internal inference will be suited to private cloud.

Consumer-facing workload spikes are likely suited to use of public cloud, with  high-volume inference likely an option for high-volume, sustained inference operations.

Magnifica Humanitas is Not "Just" About AI

In spite of all the attention received by Magnifica Humanitas, focused on the relationship between human values and artificial intelligence, it also can be viewed within the context of the full range of social doctrine guiding the Catholic Church since the publishing of Rerum Novarum in 1891. 

Note the intentional choice of name by Pope Leo XIV (who authored Magnifica Humanitas), following Pope Leo XIII (who authored Rerum Novarum). 

The choice of name is deliberate. It implies that his pontificate will center on the Church aggressively defending human dignity and workers' rights.

Every encyclical since Rerum Novarum has elaborated a consistent body of doctrine known as the “social doctrine” of the church, compiled neatly in the document Compendium of the Social Doctrine of the Church. 

The thread running through every encyclical is that the human person possesses an inalienable dignity and that no economic system, political ideology, technological or ecological issue can be evaluated apart from what it does to that dignity:

• Rerum Novarum (1891) argues that workers are not a commodity and have the right to a just wage, the right to form associations and own property.

• Quadragesimo Anno (1931) includes the principle that decisions should be made at the lowest competent level of social organization, to support human agency and creativity.

• Mater et Magistra (1961) discusses human rights and responsibilities

• Pacem in Terris (1963) argues that states, like persons, are subject to a moral order they did not create

• Populorum Progressio (1967) argues that authentic economic development is not gross domestic product  growth but the development of the whole person and every person

• Octogesima Adveniens (1971) addresses the person in the modern city — uprooted from traditional communities, confronted with ideological pluralism, newly exposed to media and mass politics

• Laborem Exercens (1981) argues that work matters not only because it produces goods, but because in working, the person expresses and develops themselves as a subject

• Sollicitudo Rei Socialis discusses solidarity, a commitment to the common good

• Centesimus Annus (1991) argues that markets are legitimate when governed by a strong juridical framework and embedded in a culture that prizes more than consumption and is shaped by concern for the common good

• Caritas in Veritate (2009) insists that love (caritas) belongs in economic and social analysis as a principle of social architecture. He argues that the economy needs not only justice (what is owed) but gift (what is freely given beyond obligation)

• Laudato Si' (2015) argues that the ecological crisis and the human dignity crisis are the same crisis. An economy and culture that exploits the natural world with indifference is animated by the same logic that exploits persons

• Laudate Deum (2023) is about climate change and global governance

• Dilexit Nos (2024) argues that  persons are not bundles of preferences or economic actors but beings with an interior life oriented toward love

• Magnifica Humanitas (2026) extends the principle of human dignity to the use of AI. 

A persistent misreading of Catholic social encyclicals treats them as policy manifestos. 

They are instead a platform of moral principles that guide policy reasoning without determining specific policy outcomes.

In other words, the encyclicals teach ends. They do not generally prescribe the means.

The principles most commonly discussed in social teaching include:

• Human dignity: Every person, by virtue of being human, deserves to be treated as an end, never merely as a means

• The common good:  conditions that allow persons and communities to flourish

• Subsidiarity: decisions should be made at the lowest level of social organization competent to make them effectively

• Solidarity: persons and communities are genuinely responsible for one another

• The Universal Destination of Goods: the goods of creation are destined for all persons

• The Preferential Option for the Poor: in situations of conflict or scarcity, priority belongs to the needs of the most vulnerable.

The corollary is a high and demanding vocation for the laity. If the Church does not prescribe policies, then Catholic politicians, economists, lawyers, scientists, engineers, and citizens bear genuine responsibility for the quality of their prudential reasoning.

The Catholic tradition, outlined in Gaudium et Spes, does explicitly and formally hold that the determination of specific social, economic, and political policies falls within the proper competence of the laity, and that clergy, as clergy, have no special expertise in these matters.

Apostolicam Actuositatem (Decree on the Apostolate of the Laity) reinforces this, saying "the laity must take up the renewal of the temporal order as their own special obligation."

Christifideles Laici (1988) is perhaps the most fully developed treatment. The apostolic exhortation describes the laity's secular character as their "proper and peculiar character.” It is not accidental but essential to their vocation. 

The temporal order (politics, economics, culture, science, the professions) is the specific field of lay apostolic activity, according to Octogesima Adveniens. Clergy and religious who involve themselves in direct political activity are, in an important sense, trespassing on a domain that is not properly theirs.

But there are nuances. 

It does not mean clergy are forbidden from having opinions. Bishops and priests are citizens, often educated people, sometimes with relevant expertise in economics, law, or political philosophy quite apart from their ordination. A bishop who is also a trained economist has that economic competence as a person.

It does not mean the Church is silent on social matters. The entire tradition of social encyclicals demonstrates the opposite. What it means is that the Church's legitimate social teaching operates at the level of moral principles, not policy. 

The Catholic tradition's formal position is both clear and demanding: clergy have genuine and important authority in articulating moral principles, forming conscience, and prophetically naming clear violations of human dignity. 

The clergy has no special competence, and no legitimate authority, in determining the specific social, economic, and political policies through which those principles are imperfectly and provisionally embodied in historical institutions.

Magnifica Humanitas has to be read with all that in mind. 

AI Infra Financing Gets Creative

Financing of AI infrastructure has evolved into a complex, multi-layered financial architecture that extends well beyond traditional corporate balance sheets. 

External financing structures include:

• Strategic partnerships: Frontier model labs and hyperscalers are forming partnerships for regional development, power infrastructure, and equity contributions

• Public sector and sovereign support

• Captive markets: In some instances, state-owned enterprises or governments direct domestic demand toward local chip manufacturers.

Financing Model	Description	Example / Context	Source
Structured/Off-Balance Sheet	Using infrastructure funds and private credit to distribute risk across a layered set of claims.	General industry shift toward using private credit and structured vehicles to fund data center buildouts.	BIS
Community-First Partnerships	Joint commitments between developers and providers to share infrastructure costs and regional responsibilities.	Microsoft's "Community-First AI Infrastructure" plan and OpenAI's "Stargate Community" initiative.	HKS
National Sovereign Investment	Coordinating investments in data, compute, and algorithms through sovereign-backed frameworks.	Frameworks for "AI Triads" in low-to-middle-income countries using structured funding tranches.	Oxford
Captive Market Funding	Generating revenues through domestic mandated demand to fund internal R&D cycles.	Huawei’s AI chip revenue generation within the Chinese domestic ecosystem.	Bruegel

In many instances, the intent is to reduce capital investment requirements by moving to off balance sheet vehicles or “compute as payment” arrangements.

Hyperscaler	Model Supplier	Deal Type / Structure	Estimated Value / Capacity	Source
Google	Anthropic	Multi-year compute commitment + Equity investment	Up to $40B investment; 3.5GW TPU capacity (via Broadcom)	Silicon Republic
Amazon (AWS)	Anthropic	Compute credit + Equity investment	Up to $25B total commitment; multi-year cloud compute	Silicon Republic
Microsoft	OpenAI	Exclusive cloud provider + Multi-stage capital injection	~$10B+ in multi-year funding; 49% profit stake	Aranca
Meta	N/A (Self-build)	Structured finance (SPV) for data center buildout	~$30B "Hyperion" SPV (Blue Owl Capital led)	SoftwareSeni
Google/Anthropic	SpaceX	Compute infrastructure delivery contracts	Potentially >$70B over multi-year term	AA

As seen with Meta’s "Hyperion" transaction, hyperscalers are increasingly utilizing Special Purpose Vehicles (SPVs) and partnerships with private credit firms (e.g., Blue Owl Capital) to fund massive data center buildouts. This allows the companies to offload the capital intensity of the physical build while retaining operational control and capacity priority.

In many of these deals, "compute" has become a literal form of payment. The Google-Anthropic and Amazon-Anthropic deals are not merely cash-for-equity; they are deeply intertwined with multi-gigawatt (GW) capacity commitments and customized hardware access (such as Google’s TPUs).

Financing is no longer focused just on chips. The capital is increasingly directed toward the "AI Triad"—the integration of compute, dedicated energy infrastructure, and data center physical shells. This is evidenced by the trend of co-locating data centers with renewable energy sources and the invocation of national defense acts (as seen in the U.S. in early 2026) to prioritize grid expansion for AI.